Project Summary The long-term goal of this study is to optimize a new rapid, quantitative imaging approach utilizing lightsheet microscopy in order to dramatically improve the 3D and 4D cellular information obtainable from mouse models of eye disease. During retinal disease, significant changes in cell and tissue morphology are common. In retinopathies for example, excessive, thickened, bulbous, leaky blood vessels and abnormal `tufts' form, protruding out of their usual layered locations. These malformed vessels cause many problems including the generation of abnormal mechanical traction, which pulls on the different layers of the eye, eventually causing the retina to detach. Understanding how and why cells grow into abnormal three-dimensional structures is key to understanding retinal disease progression and treatment. However, current imaging techniques used to investigate retinas at the cellular level are limited in terms of 3D information, due to a practical issue that the naturally spherical eye tissue must be flattened to be viewed on a slide under the microscope. We therefore propose to optimize the first lightsheet microscope protocol for mouse eye imaging as its design permits imaging of large intact tissues, in their natural form, avoiding distortion through flattening of the 3D tissue. Furthermore, the faster dissection method and rapid image acquisition (less than a minute to image an entire eye) of the lightsheet microscope enables us to pursue the first robust method for live imaging of cell behavior in the eye. Previous attempts at live imaging have seen limited success due again to the excessive flattening and distortion of the spherical eye tissue required to transfer it onto a dish for conventional microscopy. In storing high-resolution cellular information across large sections of eye tissue, and potentially also over many time points in dynamic imaging, lightsheet microscopes generate very large datasets. The Big Data issues incurred can cause significant scaling issues for standard image analysis software. We therefore propose to develop easy to use, freely available computational software tools alongside optimization of the imaging protocol in order to facilitate rapid adoption of the technique and maximize the quantitative information obtainable by the wider community of eye disease researchers. Altogether we call this idea of an integrated 3D-4D imaging and analysis method the ?Eye-dea approach?, standing for Eye ? preserved Dimensional tissue Environment Analysis. In this study we propose to demonstrate the advantages of the Eye-dea approach by providing the first characterization of 3D and 4D cellular abnormalities in a retinopathy mouse model. Furthermore we will demonstrate that the approach can overcome a current barrier to therapeutic progress: a lack of 3D cellular imaging tools. Our test case is a potentially restorative glaucoma stem cell therapy. We will quantify the level of functional integration of neuronal progenitor cells into the retinal layers of the eye, not feasible with current imaging methods.